Recent developments in computers and corresponding speech recognition software algorithms have made it possible to control equipment via spoken commands. Thus, it is becoming more feasible and common for users to control their computers, electronics, personal devices, etc., via speech input.
Speech recognition systems are highly complex and operate by matching an acoustic signature of an utterance with acoustic signatures of words in a language model. As an example, according to conventional speech recognition systems, a microphone first converts a received acoustic signature of an uttered word into an electrical signal. An A/D (analog-to-digital) converter is typically used to convert the electrical signal into a digital representation. A digital signal processor converts the captured electrical signal from the time domain to the frequency domain.
Generally, as another part of the speech recognition process, the digital signal processor breaks down the utterance into its spectral components. The amplitude or intensity of the digital signal at various frequencies and temporal locations are then compared to a language model to determine the one or more word that were uttered.
Speech recognition systems typically become less effective as the size of a vocabulary to be recognized by the model increases. Accordingly, for language models supporting a large vocabulary, it is more likely that a word will not be properly recognized. In some speech recognition systems, in order to limit complexity of a language model, the vocabulary supported by a respective model can be limited.